IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0233686.html
   My bibliography  Save this article

A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

Author

Listed:
  • Lucas Wirbka
  • Walter E Haefeli
  • Andreas D Meid

Abstract

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient’s characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients.

Suggested Citation

  • Lucas Wirbka & Walter E Haefeli & Andreas D Meid, 2020. "A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-12, May.
  • Handle: RePEc:plo:pone00:0233686
    DOI: 10.1371/journal.pone.0233686
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233686
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0233686&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0233686?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Joon Lee & David M Maslove & Joel A Dubin, 2015. "Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    2. Xia Jiang & Alan Wells & Adam Brufsky & Richard Neapolitan, 2019. "A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-18, March.
    3. Joseph E Lucas & Taylor C Bazemore & Celan Alo & Patrick B Monahan & Deepak Voora, 2017. "An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andreas D. Meid & Lucas Wirbka, 2022. "Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants," Medical Decision Making, , vol. 42(5), pages 587-598, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tan, Tu Guang & Jang, Sunghyon & Yamaguchi, Akira, 2019. "A novel method for risk-informed decision-making under non-ideal Instrumentation and Control conditions through the application of Bayes’ Theorem," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 463-472.
    2. Christos T Nakas & Narayan Schütz & Marcus Werners & Alexander B Leichtle, 2016. "Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
    3. YoungJin Choi & YooKyung Boo, 2020. "Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality," IJERPH, MDPI, vol. 17(3), pages 1-10, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0233686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.